„In conclusion, I found reading Metaheuristics: FromDesign to Implementation to be pleasant and enjoyable. Iparticularly recommend it as a reference for researchers andstudents of computer science or operations research who want aglobal outlook of metaheuristics methods. It would also beextremely useful for introducing graduate and PhD students who arenew to the field of heuristics and metaheuristics to the amazingworld of the designing of these procedures.“ (Informs, 1 July 2012)
„It will be an indispensable text for advanced undergraduate andgraduate students in computer science, operations research, appliedmathematics, control, business and management and engineering.“(Zentralblatt MATH, 2010)
A unified view of metaheuristics
This book provides a complete background on metaheuristics andshows readers how to design and implement efficient algorithms tosolve complex optimization problems across a diverse range ofapplications, from networking and bioinformatics to engineeringdesign, routing, and scheduling. It presents the main designquestions for all families of metaheuristics and clearlyillustrates how to implement the algorithms under a softwareframework to reuse both the design and code.
Throughout the book, the key search components of metaheuristicsare considered as a toolbox for:
* Designing efficient metaheuristics (e. g. local search, tabusearch, simulated annealing, evolutionary algorithms, particleswarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems
* Designing efficient metaheuristics for multi-objectiveoptimization problems
* Designing hybrid, parallel, and distributed metaheuristics
* Implementing metaheuristics on sequential and parallelmachines
Using many case studies and treating design and implementationindependently, this book gives readers the skills necessary tosolve large-scale optimization problems quickly and efficiently. Itis a valuable reference for practicing engineers and researchersfrom diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and appliedmathematics.
This book provides a complete background on metaheuristics andshows readers how to design and implement efficient algorithms tosolve complex optimization problems across a diverse range ofapplications, from networking and bioinformatics to engineeringdesign, routing, and scheduling. It presents the main designquestions for all families of metaheuristics and clearlyillustrates how to implement the algorithms under a softwareframework to reuse both the design and code.
Throughout the book, the key search components of metaheuristicsare considered as a toolbox for:
* Designing efficient metaheuristics (e. g. local search, tabusearch, simulated annealing, evolutionary algorithms, particleswarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems
* Designing efficient metaheuristics for multi-objectiveoptimization problems
* Designing hybrid, parallel, and distributed metaheuristics
* Implementing metaheuristics on sequential and parallelmachines
Using many case studies and treating design and implementationindependently, this book gives readers the skills necessary tosolve large-scale optimization problems quickly and efficiently. Itis a valuable reference for practicing engineers and researchersfrom diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and appliedmathematics.
